Qwen3.5 9B needs ~9.8 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~45 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
44.9 tok/s
TTFT
4316 ms
Safe context
231K
Memory
9.8 GB / 24.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 44.9 tok/s | 2354 ms | 231K |
| Coding | C | Runs well | 44.9 tok/s | 4316 ms | 231K |
| Agentic Coding | C | Runs well | 44.9 tok/s | 6278 ms | 231K |
| Reasoning | C | Runs well | 44.9 tok/s | 5101 ms | 231K |
| RAG | C | Runs well | 44.9 tok/s | 7848 ms | 231K |
How Qwen3.5 9B (9B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C45 |
Q3_K_S | 3 | 4.4 GB | Low | C46 |
NVFP4 | 4 | 5.0 GB | Medium | C46 |
Q4_K_M | 4 | 5.5 GB | Medium | C46 |
Q5_K_M | 5 | 6.5 GB | High | C47 |
Q6_K | 6 | 7.4 GB | High | C47 |
Q8_0 | 8 | 9.6 GB | Very High | C49 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C50 |
Copy-paste commands to run Qwen3.5 9B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "unsloth/Qwen3.5-9B-GGUF" \
--hf-file "Qwen3.5-9B-GGUF-Q4_K_M.gguf" \
-c 4096 -ngl 99Opciones de mejora
RTX 5090 32GBOpción económicaSube la velocidad estimada de decodificación alrededor de un 281%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
No es solo un salto de hardware. También te deja en un ecosistema de runtimes más limpio para LLMs locales.
~$1,999 MSRP
~$2,499 MSRP
Yes, Intel Arc Pro B60 24GB can run Qwen3.5 9B with a C grade (Runs well). Expected decode speed: 44.9 tok/s.
Qwen3.5 9B (9B parameters) requires approximately 9.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3.5 9B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro B60 24GB, Qwen3.5 9B achieves approximately 44.9 tokens per second decode speed with a time-to-first-token of 4316ms using Q4_K_M quantization.
For coding workloads, Qwen3.5 9B on Intel Arc Pro B60 24GB receives a C grade with 44.9 tok/s and 231K context.
On Intel Arc Pro B60 24GB, Qwen3.5 9B can safely use up to 231K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-unsloth--qwen3-5-9b-gguf-on-arc-pro-b60-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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